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Biological information flow
Information flow in biological systems (Source Central dogma of biology on Wikipedia).
There are three main biological entities that respectively store information, act as data intermediates, and the functional units, and the figure below show how information flows between these three levels.
DNA, that lives in the nucleus of cells, is the central information storage mechanism, and encodes the blueprint of the functional units as genes. DNA is transcribed into messenger RNA (mRNA), that re-localises outside the nucleus and is further processed into its mature exon-only form after removal of the non-coding introns sequences. Finally, the mRNA is translated by the ribosomial machinery into proteins directly into the endoplasmic reticulum (ER) where they are then redirected to their final destination.
Sub-cellular structure of an animal cell (Source Cell biology on Wikipedia).
In addition to the standard information worflow where DNA is transcribed into RNA that itself is translated into proteins, information flow, there is also reverse transcription, that generates (complementary) DNA from and RNA molecule, as well as replication of DNA (during cell division) and RNA molecules.
Why studying proteins
Proteins as the functional units in all living organisms, and they are highly dynamic. The caterpillar and the resulting butterfly have the same genome. The complement of all the expressed proteins, termed the proteome is however very different.
The metamorphosis from a caterpilar to a monarch butterfly. (Image from Phys.prg)
There are different modalities of the proteome that are of interest. In addition to the presence or amount of protein in a biological samples, it is also important to study the interactions between proteins forming protein-protein complexes, the presence of post-transcriptional modification (such as, for example, phosphorylations), the rate at which proteins are produced and degrated, or where the proteins reside inside a cell.
The technique of choice to study proteins in a high throughput way is mass spectrometry.
Setup
The MSnbase package.
We are going to use the Bioconductor (Huber et al. 2015Huber, W, V J Carey, R Gentleman, S Anders, M Carlson, B S Carvalho, H C Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nat. Methods 12 (2): 115–21.)MSnbase package (L. Gatto and Lilley 2012Gatto, Laurent, and Kathryn S Lilley. 2012. “MSnbase-an R/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28 (2): 288–89.), which can be install with the BiocManager package, available from CRAN. If BiocManager isn’t available on your computer, install it with:
install.packages("BiocManager")
Now, install MSnbase and its dependencies with
BiocManager::install("MSnbase")
For additional information on how to analyse mass spectrometry-based proteomics data, refer to (Gatto and Christoforou 2014Gatto, L, and A Christoforou. 2014. “Using R and Bioconductor for Proteomics Data Analysis.” Biochim. Biophys. Acta 1844 (1 Pt A): 42–51.) and (L. Gatto 2019Gatto, Laurent. 2019. Bioconductor Tools for Mass Spectrometry and Proteomics. https://rawgit.com/lgatto/bioc-ms-prot/master/lab.html.), or explore the the proteomics- and mass spectrometry-related packages on the Bioconductor page
How does mass spectrometry work?
Mass spectrometry (MS) is a technology that separates charged molecules (ions) based on their mass to charge ratio (M/Z). It is often coupled to chromatography (liquid LC, but can also be gas-based GC). The time an analytes takes to elute from the chromatography column is the retention time.
A chromatogram, illustrating the total amount of analytes over the retention time.
An mass spectrometer is composed of three components:
The source, that ionises the molecules: examples are Matrix-assisted laser desorption/ionisation (MALDI) or electrospray ionisation. (ESI)
The analyser, that separates the ions: Time of flight (TOF) or Orbitrap.
The detector that quantifies the ions.
When using mass spectrometry for proteomics, the proteins are first digested with a protease such as trypsin. In mass shotgun proteomics, the analytes assayed in the mass spectrometer are peptides.
Often, ions are subjected to more than a single MS round. After a first round of separation, the peaks in the spectra, called MS1 spectra, represent peptides. At this stage, the only information we possess about these peptides are their retention time and their mass-to-charge (we can also infer their charge be inspecting their isotopic envelope, i.e the peaks of the individual isotopes, see below), which is not enough to infer their identify (i.e. their sequence).
In MSMS (or MS2), the settings of the mass spectrometer are set automatically to select a certain number of MS1 peaks (for example 20). Once a narrow M/Z range has been selected (corresponding to one high-intensity peak, a peptide, and some background noise), it is fragmented (using for example collision-induced dissociation (CID), higher energy collisional dissociation (HCD) or electron-transfer dissociation (ETD)). The fragment ions are then themselves separated in the analyser to produce a MS2 spectrum. The unique fragment ion pattern can then be used to infer the peptide sequence using de novo sequencing (when the spectrum is of high enough quality) of using a search engine such as, for example Mascot, MSGF+, …, that will match the observed, experimental spectrum to theoratical spectra (see details below).
Schematics of a mass spectrometer and two rounds of MS.
The animation below show how 25 ions different ions (i.e. having different M/Z values) are separated throughout the MS analysis and are eventually detected (i.e. quantified). The final frame shows the hypothetical spectrum.
Separation and detection of ions in a mass spectrometer.
The figures below illustrate the two rounds of MS. The spectrum on the left is an MS1 spectrum acquired after 21 minutes and 3 seconds of elution. 10 peaks, highlited by dotted vertical lines, were selected for MS2 analysis. The peak at M/Z 460.79 (488.8) is highlighted by a red (orange) vertical line on the MS1 spectrum and the fragment spectra are shown on the MS2 spectrum on the top (bottom) right figure.
Parent ions in the MS1 spectrum (left) and two sected fragment ions MS2 spectra (right).
The figures below represent the 3 dimensions of MS data: a set of spectra (M/Z and intensity) of retention time, as well as the interleaved nature of MS1 and MS2 (and there could be more levels) data.
MS1 spectra (blue) over retention time (left). MS2 spectra (pink) interleaved between two MS1 spectra (right),
Reading and accessing MS data
Let’s read a very small raw MS data file into R using the readMSData from the MSnbase package. The file that we are going to load is also available in the package.
## MSn experiment data ("MSnExp")
## Object size in memory: 0.18 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 5
## MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Sun Mar 24 22:36:53 2019
## MSnbase version: 2.9.3
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: F1.S1 F1.S2 ... F1.S5 (5 total)
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
The object that is returned by readMSData is of class MSnExp, that can store, access and manipulate raw MS data. Note that here we are focusing on MS-based proteomics data, but this also applied to MS-based metabolomics data.
We can find out how many spectra are available in that data using the function length. Full MS acquisitions would contain hundreds of thousands spectra.
length(x)
## [1] 5
We can use various accessor function to get the MS level of these spectra, their retention time, or the M/Z and intensity of the precursor peaks of the ion corresponding to the MS2 spectra.
We can also extract individual spectra using [[ and plot them.
x[[3]]
## Object of class "Spectrum2"
## Precursor: 645.3741
## Retention time: 25:2
## Charge: 2
## MSn level: 2
## Peaks count: 2125
## Total ion count: 150838188
plot(x[[3]])
Visualisation of the 3rd MS spectrum in our small test data set.
Exercise
For the rest of this tutorial, we will be using a slightly larger dataset (still tiny compared to full acquisitions) that is distributed with the MSnbase package. Load it as shown below and compute the number of spectra available in that dataset, their MS level, and the retention time range over which these spectra have been acquired.
data(itraqdata)
length(itraqdata)
## [1] 55
unique(msLevel(itraqdata))
## [1] 2
formatRt(range(rtime(itraqdata)))
## [1] "19:9" "50:18"
This object also contains additional metadata for each spectrum, that can be accessed, as a data.frame, with fData.
Identification
The raw data is still a long way of obtaining biologically relevant proteomics data. The first step to obtain proteomics data is to identify the peptides that have been acquired in the MS. Peptide identification work by comparing expected and observed spectra. As shown below, when a precursor peptide ion is fragmented in a CID cell, it breaks at specific bonds, producing sets of peaks (a, b, c and x, y, z) that can be predicted.
Peptide fragmentation.
It is thus possible to calculate the expected set of fagment peaks for a given peptide, such as SIGFEGDSIGR below.
calculateFragments("SIGFEGDSIGR")
## mz ion type pos z seq
## 1 88.03931 b1 b 1 1 S
## 2 201.12337 b2 b 2 1 SI
## 3 258.14483 b3 b 3 1 SIG
## 4 405.21324 b4 b 4 1 SIGF
## 5 534.25583 b5 b 5 1 SIGFE
## 6 591.27729 b6 b 6 1 SIGFEG
## 7 706.30423 b7 b 7 1 SIGFEGD
## 8 793.33626 b8 b 8 1 SIGFEGDS
## 9 906.42032 b9 b 9 1 SIGFEGDSI
## 10 963.44178 b10 b 10 1 SIGFEGDSIG
## 11 175.11895 y1 y 1 1 R
## 12 232.14041 y2 y 2 1 GR
## 13 345.22447 y3 y 3 1 IGR
## 14 432.25650 y4 y 4 1 SIGR
## 15 547.28344 y5 y 5 1 DSIGR
## 16 604.30490 y6 y 6 1 GDSIGR
## 17 733.34749 y7 y 7 1 EGDSIGR
## 18 880.41590 y8 y 8 1 FEGDSIGR
## 19 937.43736 y9 y 9 1 GFEGDSIGR
## 20 1050.52142 y10 y 10 1 IGFEGDSIGR
## 21 873.42266 b9_ b_ 9 1 SIGFEGDSI
## 22 930.44412 b10_ b_ 10 1 SIGFEGDSIG
## 23 514.28579 y5_ y_ 5 1 DSIGR
## 24 571.30725 y6_ y_ 6 1 GDSIGR
## 25 700.34984 y7_ y_ 7 1 EGDSIGR
## 26 847.41825 y8_ y_ 8 1 FEGDSIGR
## 27 904.43971 y9_ y_ 9 1 GFEGDSIGR
## 28 1017.52377 y10_ y_ 10 1 IGFEGDSIGR
## 29 142.12130 y1_ y_ 1 1 R
## 30 199.14276 y2_ y_ 2 1 GR
## 31 312.22682 y3_ y_ 3 1 IGR
## 32 399.25885 y4_ y_ 4 1 SIGR
The last step is to compare obseved and expected peaks. If there is a good match, the MS2 spectrum is assigned the peptide sequence.
itraqdata2 <- pickPeaks(itraqdata, verbose = FALSE)
s <- "SIGFEGDSIGR"
plot(itraqdata2[[14]], s, main = s)
Matching observed and expected peaks.
It is also possible to plot 2 spectra to compare them directly.
In a full experiment, all possible peptides from the known (or relevant) proteome of interest (such as databases that can be downloaded from the UniProt site1 The Universal Protein Resource (UniProt) is a freely and accessible comprehensive resource for protein sequence and annotation data.) are compared to the millions of observed spectra.
From the list of identified petides, it is then necessary to infer the most propable proteins that were present in the biological sample.
Exercise
Plot the 44th spectrum of the itraqdata2 experiment. The sequence can be accessed in the feature metadata with
plot(itraqdata2[[44]], s,
main = fData(itraqdata2)$PeptideSequence[[44]])
Quantitation
The last step of MS data processing is to quantify peptide abundances in the biological samples. The table below summarises the different possibilites depending whether the proteins or peptides are labelled, and whether the quantitation is performed in MS1 or MS2.
Label-free
Labelled
MS1
XIC
SILAC, 15N
MS2
Counting
iTRAQ, TMT
Label-free MS2: Spectral counting
In spectral counting, on simply counts the number of quantified peptides that are assigned to a protein.
Labelled MS2: Isobaric tagging
Isobaric tagging refers to the labelling using isobaric tags, i.e. chemical tags that have the same mass and hence can’t be distinguish by the spectrometer. The peptides of different samples (4, 6, 10 or 11) are labelled with different tags and combined prior to mass spectrometry acquisition. Given that they are isobaric, all identical peptides, irrespective of the tag and this the sample of origin, are co-analysed, up to fragmentation prior to MS2 analysis. During fragmentation, the isobaric tags fall of, fragment themselves, and result in a set of sample specific peaks. These specific peaks can be used to infer sample-specific quantitation, while the rest of the MS2 spectrum is used for identification.
Label-free MS1: extracted ion chromatograms
In label-free quantitation, the precursor peaks that match an identified peptide are integrated of retention time and the area under that extracted ion chromatogram is used to quantify that peptide in that sample.
In SILAc quantitation, sample are grown in a medium that contains heavy amino acids (typically arginine and lysine). All proteins gown in this heavy growth medium contain the heavy form of these amino acids. Two samples, one grown in heavy medium, and one grown in normal (light) medium are then combined and analysed together. The heavy peptides precursor peaks are systematically shifted compared to the light ones, and the ratio between the height of a heavy and light peaks can be used to calculate peptide and protein fold-changes.
Figure: credit Wikimedia Commons.
Exercise
As its name implies, the itraqdata is an iTRAQ-based isobar quantitation experiment. We can visualise the reporter peaks as follows:
plot(itraqdata[[14]], reporters = iTRAQ4, full = TRUE)
Visualisation of the iTRAQ reporter peaks.
We can quantify these four peaks with the quantify method, to produce and object of class MSnSet containing quantitation data. The quantitation values can be accessed with exprs. This data also contains feature metadata that can be accessed with the fData function.
In our examples, we not have processing data for the 55 peptides and 4 samples. In this data, there is only 1 missing value, corresponding to an absent reporter peak. We are going to simply drop that feature.
table(is.na(exprs(msnset)))
##
## FALSE TRUE
## 219 1
msnset <- filterNA(msnset)
In MS1 label-free experiments, given that each sample is acquired independently, the proportion of missing values can be as high several tens of percent. In such situations, removing rows with missing values isn’t possible at all. Imputation is possible, albeit tricky, as different mechanisms can be responsible for missing value that appear either at random or not at random (Lazar et al. 2016Lazar, C, L Gatto, M Ferro, C Bruley, and T Burger. 2016. “Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.” J Proteome Res 15 (4): 1116–25. doi:10.1021/acs.jproteome.5b00981.).
Next, we aggregate the spectrum-level quantitation values into protein-level data using the median and the combineFeatures function:
Following on from here, many data processing such as normalisation, non-specific filtering, and hypothesis testing is very similar to other omics data.
Applications in statistical learning
The pRoloc package.
In this section, I illustrate a use case of mass spectrometry-based proteomics to infer sub-cellular protein localisation and the application of statistical learning. This section requires the pRoloc and pRolocdata packages (Gatto et al. 2014Gatto, L, L M Breckels, S Wieczorek, T Burger, and K S Lilley. 2014. “Mass-Spectrometry-Based Spatial Proteomics Data Analysis Using pRoloc and pRolocdata.” Bioinformatics 30 (9): 1322–4. doi:10.1093/bioinformatics/btu013.; Breckels et al. 2016Breckels, L M, C M Mulvey, K S Lilley, and L Gatto. 2016. “A Bioconductor Workflow for Processing and Analysing Spatial Proteomics Data.” F1000Res 5: 2926. doi:10.12688/f1000research.10411.2.). Both packages can be installed with the BiocManager::install function, as illustrated above.
library("pRoloc")
library("pRolocdata")
The hyperLOPIT2015 data contains localisation data for 5032 proteins in mouse embryonic stem cells (Christoforou et al. 2016Christoforou, A, C M Mulvey, L M Breckels, A Geladaki, T Hurrell, P C Hayward, T Naake, et al. 2016. “A Draft Map of the Mouse Pluripotent Stem Cell Spatial Proteome.” Nat Commun 7 (January): 8992. doi:10.1038/ncomms9992.). The protein information is collected along a 20 fraction density gradient - our data matrix has dimensions 5032 rows and 20 columns. We use PCA to easily visualise it in 2 dimensions.
data(hyperLOPIT2015)
## set colours
setStockcol(paste0(getStockcol(), 80))
## produce PCA plot
plot2D(hyperLOPIT2015)
Mouse stem cell spatial proteomics data from Christoforou et al.
Each dot on the PCA plot represents a single protein. The protein is either of unknown localisation, and represented by a grey circle, or is of known localisation (and called an organelle marker) and is coloured according to its expected sub-cellular localisation.
In the next figure, we have trained an support vector machine (SVM) model and classified proteins of unknown localisation using an SVM model. The size of the dots are scaled according to the classifier score and only assignments corresponding to a false discovery rate of 5% have been considered.
Information flow in biological systems (Source Central dogma of biology on Wikipedia).
The 
The 
